Names of the Collaborators

• TRINAY GANGISETTY
• EVAN McCORMICK
• JULIA GALLWITZ
• AISHWARYA PURANIK
• SAWANI HEJIB

Link to Github Repository

https://github.com/CU-DTSC-5301-Report-JEAST/Data-Science-In-Finance-COVID-Report

Statement of the question of the interest

• In economics, an exogenous shock is a significant and unpredictable event that greatly impacts a local or global economy. An exogenous shock can be an event such as an earthquake, a flood, or the COVID-19 pandemic. When an exogenous shock occurs, governments must implement targeted policy measures to ensure the stability of the economy and the security of their citizens.

• From travel and communication, to trade and supply chain expansion, our world has never been more connected than it is now and the COVID-19 pandemic was a first-of-its-kind event in modern times. In early 2020, governments all across the globe were required to react quickly to this unprecedented exogenous shock, as it significantly disrupted the established financial processes and threatened the stability of the global economy. The following analysis will investigate the various financial sector policy measures implemented at the country level and globally in the initial months of the COVID-19 pandemic.

Data source and Description

• The data set is primarily sourced from public information collected by the World Bank and various COVID-19 financial response trackers maintained by entities such as the Yale Program on Financial Stability, IDB, IIF, IMF, and OECD.
• This comprehensive dataset meticulously documents the diverse array of policy measures enacted by governments globally in response to the challenges posed by the COVID-19 pandemic on the financial sector.
• The dataset classifies these policies into five primary domains:
  • Banking sector
  • Financial markets and institutions
  • Insolvency
  • Liquidity/funding and
  • Payments systems.

• Within each of these overarching categories, a detailed breakdown into sub-categories offers a nuanced view of the specific strategies employed.

  1. In the banking sector, policies encompass crisis management, integrity, operational continuity, prudential measures, and support for borrowers.
  2. In financial markets and institutions, sub-categories include market functioning, non-bank financial institutions (NBFI), and public debt management.
  3. The insolvency category comprises measures related to amending bankruptcy filing obligations, enhancing tools for out-of-court debt restructuring and workouts, and other insolvency-related actions. Liquidity and funding measures extend to asset purchases, liquidity (including foreign exchange), policy rate adjustments, and other liquidity-related interventions.
  4. Lastly, the payments systems sector includes sub-categories like consumer protection, easing regulatory requirements, promoting and ensuring the availability of digital payment mechanisms, and other payment-related policies.

• These meticulously organized sub-categories provide invaluable insights into the multifaceted policy responses implemented worldwide during the pandemic.

• Moreover, these sub-categories are further refined into granular classifications, adding depth to the dataset's granularity.
• Notable data points encompass the precise date of policy announcement, the responsible authority tasked with implementation, references to the public sources of these announcements, the termination dates of individual policies, and identification of measures that represent modifications or extensions of existing ones.

Data Preprocessing and Data Analysis

# importing libraries

import numpy as np
import pandas as pd

# Encoding the file

covid_data = pd.read_csv("covid-fci-data.csv", encoding = 'Latin1')

#Converting the column names to upper case

covid_data.columns = covid_data.columns.str.upper()

#examining the data set

covid_data.head()

	ID	COUNTRY NAME	COUNTRY ISO3	INCOME LEVEL	AUTHORITY	DATE	LEVEL 1 POLICY MEASURES	LEVEL 2 POLICY MEASURES	LEVEL 3 POLICY MEASURES	DETAILS OF THE MEASURE	REFERENCE	TERMINATION DATE	MODIFICATION OF PARENT MEASURE	PARENT MEASURE
0	1	China	CHN	Upper middle income	SUP	01-02-2020	Banking sector	Operational continuity ...	Blank ...	Require banks and insurance companies to ensur...	http://www.gov.cn/zhengce/zhengceku/2020-02/03...	NaN	No	NaN
1	2	Canada	CAN	High income	CB	13-03-2020	Liquidity/funding	Liquidity (incl FX)/ELA ...	Blank ...	BoC also created a Bankers Acceptance Purchase...	https://www.bankofcanada.ca/markets/market-ope...	31-10-2020	No	NaN
2	3	China	CHN	Upper middle income	CB	05-02-2020	Banking sector	Integrity ...	Other integrity ...	The PBC has issued Notices on AML/CFT requirem...	...	NaN	No	NaN
3	4	Thailand	THA	Upper middle income	CB	05-02-2020	Liquidity/funding	Policy rate ...	Blank ...	BOT cut its benchmark interest rate by 25 basi...	https://www.bot.or.th/English/PressandSpeeches...	NaN	No	NaN
4	5	Russian Federation	RUS	Upper middle income	CB	07-02-2020	Liquidity/funding	Policy rate ...	Blank ...	Policy rates were reduced by 25 basis points t...	https://www.cnbc.com/2020/02/07/reuters-americ...	NaN	No	NaN

# re-naming the columns

covid_data.rename(columns = {'COUNTRY NAME': 'COUNTRY_NAME',
                             'COUNTRY ISO3': 'COUNTRY_CODE',
                             'INCOME LEVEL': 'INCOME_LEVEL',
                             'LEVEL 1 POLICY MEASURES': 'LEVEL_1_POLICY_MEASURES',
                             'LEVEL 2 POLICY MEASURES': 'LEVEL_2_POLICY_MEASURES',
                             'LEVEL 3 POLICY MEASURES': 'LEVEL_3_POLICY_MEASURES',
                             'DETAILS OF THE MEASURE': 'DETAILS_OF_THE_MEASURE',
                             'TERMINATION DATE': 'TERMINATION_DATE',
                             'MODIFICATION OF PARENT MEASURE': 'MODIFICATION_OF_PARENT_MEASURE',
                             'PARENT MEASURE': 'PARENT_MEASURE'}, inplace = True)

# examining again after re-naming the columns

covid_data.head()

	ID	COUNTRY_NAME	COUNTRY_CODE	INCOME_LEVEL	AUTHORITY	DATE	LEVEL_1_POLICY_MEASURES	LEVEL_2_POLICY_MEASURES	LEVEL_3_POLICY_MEASURES	DETAILS_OF_THE_MEASURE	REFERENCE	TERMINATION_DATE	MODIFICATION_OF_PARENT_MEASURE	PARENT_MEASURE
0	1	China	CHN	Upper middle income	SUP	01-02-2020	Banking sector	Operational continuity ...	Blank ...	Require banks and insurance companies to ensur...	http://www.gov.cn/zhengce/zhengceku/2020-02/03...	NaN	No	NaN
1	2	Canada	CAN	High income	CB	13-03-2020	Liquidity/funding	Liquidity (incl FX)/ELA ...	Blank ...	BoC also created a Bankers Acceptance Purchase...	https://www.bankofcanada.ca/markets/market-ope...	31-10-2020	No	NaN
2	3	China	CHN	Upper middle income	CB	05-02-2020	Banking sector	Integrity ...	Other integrity ...	The PBC has issued Notices on AML/CFT requirem...	...	NaN	No	NaN
3	4	Thailand	THA	Upper middle income	CB	05-02-2020	Liquidity/funding	Policy rate ...	Blank ...	BOT cut its benchmark interest rate by 25 basi...	https://www.bot.or.th/English/PressandSpeeches...	NaN	No	NaN
4	5	Russian Federation	RUS	Upper middle income	CB	07-02-2020	Liquidity/funding	Policy rate ...	Blank ...	Policy rates were reduced by 25 basis points t...	https://www.cnbc.com/2020/02/07/reuters-americ...	NaN	No	NaN

# removing the white space on both the trailing ends

covid_data['LEVEL_1_POLICY_MEASURES'] = covid_data['LEVEL_1_POLICY_MEASURES'].str.strip()

# removing the white space on both the trailing ends

covid_data['LEVEL_2_POLICY_MEASURES'] = covid_data['LEVEL_2_POLICY_MEASURES'].str.strip()

# removing the white space on the trailing ends

covid_data['INCOME_LEVEL'] = covid_data['INCOME_LEVEL'].str.strip()

# Finding out the percentage that each level_2_measure contributes with in the level_1_measure

grouping_measures = covid_data.groupby(['LEVEL_1_POLICY_MEASURES','LEVEL_2_POLICY_MEASURES'])['ID'].sum().rename("Percentage").groupby(level = 0).transform(lambda x: 100 * x/x.sum())
grouping_measures = grouping_measures.reset_index()
grouping_measures['Percentage'] = np.round(grouping_measures['Percentage'], 2) # rounding the percentage
grouping_measures

	LEVEL_1_POLICY_MEASURES	LEVEL_2_POLICY_MEASURES	Percentage
0	Banking sector	Crisis management	0.73
1	Banking sector	Integrity	2.72
2	Banking sector	Operational continuity	1.29
3	Banking sector	Prudential	52.98
4	Banking sector	Support borrowers	42.27
5	Financial Markets/NBFI	Market functioning	71.33
6	Financial Markets/NBFI	NBFI	21.79
7	Financial Markets/NBFI	Public debt management	6.88
8	Insolvency	Amending bankruptcy filing obligations	86.89
9	Insolvency	Enhancing tools for out-of-court debt restruct...	10.83
10	Insolvency	Other insolvency	2.28
11	Liquidity/funding	Asset purchases	9.23
12	Liquidity/funding	Liquidity (incl FX)/ELA	66.84
13	Liquidity/funding	Other liquidity	0.51
14	Liquidity/funding	Policy rate	23.42
15	Payment systems	Consumer protection measures and ensuring avai...	7.72
16	Payment systems	Easing regulatory requirements	10.32
17	Payment systems	Other Payments	11.98
18	Payment systems	Promoting and ensuring availability of digital...	69.98

grouping_measures['LEVEL_1_POLICY_MEASURES'] = grouping_measures['LEVEL_1_POLICY_MEASURES'].str.strip()

# looking at the value counts of the level_1_policy_measures column

grouping_measures['LEVEL_1_POLICY_MEASURES'].value_counts()

Banking sector            5
Liquidity/funding         4
Payment systems           4
Financial Markets/NBFI    3
Insolvency                3
Name: LEVEL_1_POLICY_MEASURES, dtype: int64

# seperating each of the policy measure into its own variable

banking = grouping_measures[grouping_measures['LEVEL_1_POLICY_MEASURES'] == 'Banking sector']
financial = grouping_measures[grouping_measures['LEVEL_1_POLICY_MEASURES'] == 'Financial Markets/NBFI']
liquidity = grouping_measures[grouping_measures['LEVEL_1_POLICY_MEASURES'] == 'Liquidity/funding']
payments = grouping_measures[grouping_measures['LEVEL_1_POLICY_MEASURES'] == 'Payment systems']
insolvency = grouping_measures[grouping_measures['LEVEL_1_POLICY_MEASURES'] == 'Insolvency']

# setting index for each of the measure for the ease of visualization plotting

banking = banking.set_index(banking['LEVEL_2_POLICY_MEASURES'])
financial = financial.set_index(financial['LEVEL_2_POLICY_MEASURES'])
liquidity = liquidity.set_index(liquidity['LEVEL_2_POLICY_MEASURES'])
payments = payments.set_index(payments['LEVEL_2_POLICY_MEASURES'])
insolvency = insolvency.set_index(insolvency['LEVEL_2_POLICY_MEASURES'])

banking

	LEVEL_1_POLICY_MEASURES	LEVEL_2_POLICY_MEASURES	Percentage
LEVEL_2_POLICY_MEASURES
Crisis management	Banking sector	Crisis management	0.73
Integrity	Banking sector	Integrity	2.72
Operational continuity	Banking sector	Operational continuity	1.29
Prudential	Banking sector	Prudential	52.98
Support borrowers	Banking sector	Support borrowers	42.27

# pip install pywaffle (to create waffle charts coz pycharts are too boring..)

from pywaffle import Waffle
import matplotlib.pyplot as plt
import pandas as pd

# Waffle subfigure
fig = plt.figure(
    FigureClass = Waffle,
    plots = {
        # 5 figures with the axis 511, 512, 513 ...
        511: {
            'values': banking['Percentage'], 
            'labels': [f"{k} ({v} %)" for k, v in banking['Percentage'].items()], # unzipping for the labels in the legend
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': 'Banking sector', 'loc': 'left', 'fontsize': 10},
            'colors': ["#d55e00", "#cc79a7", "#0072b2", "#f0e442", "#009e73"] # using color blind friendly colours
        }, 
        512: {
            'values': financial['Percentage'],
            'labels': [f"{k} ({v} %)" for k, v in financial['Percentage'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': 'Financial Markets/NBFI', 'loc': 'left', 'fontsize': 10},
            'colors': ["#232066", "#983D3D", "#DCB732"]
        },
        513: {
            'values': insolvency['Percentage'],
            'labels': [f"{k} ({v} %)" for k, v in insolvency['Percentage'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': 'Insolvency', 'loc': 'left', 'fontsize': 10},
            'colors': ["#FEFE62", "#DC3220", "#E1BE6A"]
        },
        514: {
            'values': liquidity['Percentage'],
            'labels': [f"{k} ({v} %)" for k, v in liquidity['Percentage'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': 'Liquidity/funding', 'loc': 'left', 'fontsize': 10},
            'cmap_name': "Dark2"
        },
        515: {
            'values': payments['Percentage'],
            'labels': [f"{k} ({v} %)" for k, v in payments['Percentage'].items()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
            'title': {'label': 'Payment systems', 'loc': 'left', 'fontsize': 10},
            'cmap_name': "Paired"
        }
    },
    rows = 10,
    figsize = (10,10),
    cmap_name = "tab20b")

fig.suptitle('Breakdown of Policy measures by the Target area', fontsize = 12, fontweight = 'bold')

# plt.show()

Text(0.5, 0.98, 'Breakdown of Policy measures by the Target area')

Interpretation

• The following Breakdown of Policy measures by the Target area shows the percentage of specific policy measures for all the Level - 1 policies.
• Each small square represents 1 precent and there are 100 squares in each waffle graph which totals to 100%

Insights / Trends

From the graph we can observe that

1. When the target area is Banking Sector, the main policy actions taken were Prudential which accounts to 52.98%
   • The word Prudential means careful and avoiding risks
   • Prudential actions are intended to maintain the stability and safety of financial institutions
   • For example, in March 2020 Guyana reduced or waived fees and transactions for ATM services, loan processing, and so on as a prudential policy measure
2. When the target area is Liquidity / Funding, the main policy actions taken were Liquidity which accounts to 66.84%
   • Liquidity is selling off immovable securities to convert to liquid cash.
   • For example, in Msarch 2020 USA announced 1.5 trillion dollors in repo operations, which refers to the selling and repurchasing of government securities.
3. When the target area is Financial Markets / NBFI, the main policy actions taken were Market Functioning which accounts to 71.33%
   • The most important measure that could be taken to prevent the crash in the economy is to keep the market active and functioning.
   • NBFI (Non Banking Financial Institutions) which are private organisations play a major role in providing loans to the small businesses to keep them functioning
4. When the target area is Payment Systems, the main policy actions taken were digital payment mechanisms which accounts to 69.98%
   • There was a huge spike observed in online and digitized transactions during the pandemic all over the countries due to the restrictions imposed
   • To encourage the use of internet and promote the digitization of the country, many countries have taken several measures to revolutionize the world in the way of modernization and there by waiving of the taxes and fees for the transactions made digitally.
5. When the target area is Insolvency, the main policy actions taken were Amending Bankruptcy which accounts to 86.89%

Choice of Visualization

This would be the most convinient method to visualize the graph. because we can easily identify the measure with he highest percentage looking the waffle graph and the visual comparision would be very easy which inturn makes it very easy for the non - technical people to understand the graph better with out any efforts

# converting from string to date type

covid_data['DATE'] = pd.to_datetime(covid_data['DATE'])

C:\Users\HP\AppData\Local\Temp\ipykernel_20772\3618321463.py:3: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  covid_data['DATE'] = pd.to_datetime(covid_data['DATE'])

covid_data['DATE'].dtype

dtype('<M8[ns]')

# counting number_of_measures taken by each country globally

number_of_measures_taken = covid_data.groupby(['COUNTRY_NAME'])['ID'].count()
number_of_measures_taken = number_of_measures_taken.reset_index()
number_of_measures_taken.sort_values(by = 'ID')

	COUNTRY_NAME	ID
93	Micronesia, Fed. Sts.	1
83	Libya	1
65	Iran, Islamic Rep.	2
37	Djibouti	2
66	Iraq	2
...	...	...
133	Sri Lanka	103
74	Korea, Rep.	105
132	Spain	110
68	Italy	123
63	India	134

157 rows × 2 columns

# re-naming the column for better understanding

# pip install pycountry
number_of_measures_taken.rename(columns = {'ID':'TOTAL_MEASURES_COUNT'}, inplace = True)

number_of_measures_taken['COUNTRY_NAME'] = number_of_measures_taken['COUNTRY_NAME'].str.strip()

number_of_measures_taken.columns

Index(['COUNTRY_NAME', 'TOTAL_MEASURES_COUNT'], dtype='object')

# running a loop through the country names and attaching their respective country codes for the purpose of map visualization
# using 'plotly' library with choropleth.

import pycountry 
def alpha3code(column):
    CODE=[]
    for country in column:
        try:
            code=pycountry.countries.get(name=country).alpha_3
           # .alpha_3 means 3-letter country code 
           # .alpha_2 means 2-letter country code
            CODE.append(code)
        except:
            CODE.append('None')
    return CODE
# create a column for code 
number_of_measures_taken['CODE']=alpha3code(number_of_measures_taken.COUNTRY_NAME)
number_of_measures_taken.head()

	COUNTRY_NAME	TOTAL_MEASURES_COUNT	CODE
0	Afghanistan	8	AFG
1	Albania	17	ALB
2	Algeria	9	DZA
3	Angola	5	AGO
4	Anguilla	4	AIA

# code for plotting a map visualization

import io

import plotly.express as px

np.random.seed(12)
gapminder = number_of_measures_taken
# gapminder['counts'] = np.random.uniform(low=100000, high=200000, size=len(gapminder)).tolist()

fig = px.choropleth(gapminder, locations="CODE",
                    locationmode='ISO-3',
                    color = "TOTAL_MEASURES_COUNT", 
                    hover_name="CODE",
                    color_continuous_scale=px.colors.sequential.Reds)
fig.update_layout(title = {
    'text': "Number of Policy Measures Initiated by each country",
    'xanchor': "center",
    'yanchor': "top",
    'x': 0.44,
    'y': 0.99,
    'font_size': 20
})

fig.show()

Interpretation

• The following World map shows the Number/count of Policy Measures Initiated by each country globally.
• The darker the color, the higher the number of measures is

Insights / Trends

• From the graph we can observe that
  • India has the highest number of measures taken and the count stands at 134
  • Libya and Micronesia has the lowest measures taken and the count stands at 1 for both
• These interpretations can be determined easily with the help of colour density.
• This gives insights about the severity of the disease as well as responsibility of the government.

Choice of Visualization

This would be the most convenient method to visualize the graph. because we can easily identify the state looking the map and the visual comparison would be very easy which inturn makes it very easy for the non - technical people to understand the graph better with out any efforts

# India has highest measures taken.

gapminder.sort_values(by='TOTAL_MEASURES_COUNT', ascending=False)

	COUNTRY_NAME	TOTAL_MEASURES_COUNT	CODE
63	India	134	IND
68	Italy	123	ITA
132	Spain	110	ESP
74	Korea, Rep.	105	None
133	Sri Lanka	103	LKA
...	...	...	...
119	Samoa	2	WSM
129	Solomon Islands	2	SLB
51	Gambia, The	2	None
83	Libya	1	LBY
93	Micronesia, Fed. Sts.	1	None

157 rows × 3 columns

# Inspecting when is the first date that a particular country started taking measure and when did they stop and when was the 
# last measure taken

country_frame = covid_data.groupby(['COUNTRY_NAME'])
country_frame = country_frame.agg(Minimum_Date=('DATE', np.min), Maximum_Date=('DATE', np.max))
country_frame

	Minimum_Date	Maximum_Date
COUNTRY_NAME
Afghanistan	2020-05-27	2020-09-04
Albania	2020-01-04	2020-12-03
Algeria	2020-03-15	2020-06-04
Angola	2020-03-04	2020-10-05
Anguilla	2020-03-04	2020-03-27
...	...	...
Uzbekistan	2020-03-04	2021-11-03
Vietnam	2020-03-17	2020-12-05
West Bank and Gaza	2020-03-27	2020-09-03
Zambia	2020-01-04	2020-05-20
Zimbabwe	2020-01-05	2021-01-02

157 rows × 2 columns

covid_data.head(2)

	ID	COUNTRY_NAME	COUNTRY_CODE	INCOME_LEVEL	AUTHORITY	DATE	LEVEL_1_POLICY_MEASURES	LEVEL_2_POLICY_MEASURES	LEVEL_3_POLICY_MEASURES	DETAILS_OF_THE_MEASURE	REFERENCE	TERMINATION_DATE	MODIFICATION_OF_PARENT_MEASURE	PARENT_MEASURE
0	1	China	CHN	Upper middle income	SUP	2020-01-02	Banking sector	Operational continuity	Blank ...	Require banks and insurance companies to ensur...	http://www.gov.cn/zhengce/zhengceku/2020-02/03...	NaN	No	NaN
1	2	Canada	CAN	High income	CB	2020-03-13	Liquidity/funding	Liquidity (incl FX)/ELA	Blank ...	BoC also created a Bankers Acceptance Purchase...	https://www.bankofcanada.ca/markets/market-ope...	31-10-2020	No	NaN

# grouping the countries with respect to income class level

classes = covid_data.groupby(['COUNTRY_NAME','INCOME_LEVEL','LEVEL_1_POLICY_MEASURES','AUTHORITY'])['ID'].count()
classes = classes.reset_index()
classes

	COUNTRY_NAME	INCOME_LEVEL	LEVEL_1_POLICY_MEASURES	AUTHORITY	ID
0	Afghanistan	Low income	Banking sector	CB	5
1	Afghanistan	Low income	Liquidity/funding	CB	1
2	Afghanistan	Low income	Payment systems	CB	2
3	Albania	Upper middle income	Banking sector	CB	9
4	Albania	Upper middle income	Banking sector	GOV	1
...	...	...	...	...	...
850	Zimbabwe	Lower middle income	Banking sector	GOV	8
851	Zimbabwe	Lower middle income	Financial Markets/NBFI	GOV	1
852	Zimbabwe	Lower middle income	Financial Markets/NBFI	SUP	1
853	Zimbabwe	Lower middle income	Liquidity/funding	CB	12
854	Zimbabwe	Lower middle income	Payment systems	MoF	1

855 rows × 5 columns

# There are two same values with difference in the capitalization

classes['INCOME_LEVEL'].value_counts()

Upper middle income    284
High income            276
Lower middle income    200
Low income              87
Upper Middle Income      4
Aggregates               4
Name: INCOME_LEVEL, dtype: int64

# renaming the value so that duplicate categories can be avoided

classes['INCOME_LEVEL'] = classes['INCOME_LEVEL'].str.replace('Upper Middle Income','Upper middle income')

# inspecting after renaming

classes['INCOME_LEVEL'].value_counts()

Upper middle income    288
High income            276
Lower middle income    200
Low income              87
Aggregates               4
Name: INCOME_LEVEL, dtype: int64

# removing the authority as it has less significance

income_levels = classes.groupby(['INCOME_LEVEL','LEVEL_1_POLICY_MEASURES'])['ID'].count().rename("PERCENTAGE").groupby(level = 0).transform(lambda x: 100 * x/x.sum())
income_levels = income_levels.reset_index()
income_levels

	INCOME_LEVEL	LEVEL_1_POLICY_MEASURES	PERCENTAGE
0	Aggregates	Banking sector	50.000000
1	Aggregates	Financial Markets/NBFI	25.000000
2	Aggregates	Insolvency	25.000000
3	High income	Banking sector	48.188406
4	High income	Financial Markets/NBFI	21.376812
5	High income	Insolvency	6.521739
6	High income	Liquidity/funding	14.492754
7	High income	Payment systems	9.420290
8	Low income	Banking sector	34.482759
9	Low income	Financial Markets/NBFI	13.793103
10	Low income	Liquidity/funding	28.735632
11	Low income	Payment systems	22.988506
12	Lower middle income	Banking sector	42.500000
13	Lower middle income	Financial Markets/NBFI	14.500000
14	Lower middle income	Insolvency	4.000000
15	Lower middle income	Liquidity/funding	22.000000
16	Lower middle income	Payment systems	17.000000
17	Upper middle income	Banking sector	44.097222
18	Upper middle income	Financial Markets/NBFI	20.486111
19	Upper middle income	Insolvency	4.861111
20	Upper middle income	Liquidity/funding	19.444444
21	Upper middle income	Payment systems	11.111111

# pip install plotly
import warnings
# to disable if there are any warnings
warnings.filterwarnings("ignore")

# plotting a spyder/radar chart for different levels of income classes to look into what measures were taken the most in their 
# respective classes.

import plotly.express as px
import time
t1 = time.time()

fig = px.line_polar(income_levels, r='PERCENTAGE', theta='LEVEL_1_POLICY_MEASURES', color='INCOME_LEVEL', line_close=True, template="plotly_dark")
fig.update_traces(fill="toself")
fig.update_layout(title = {
    'text': "Target Area Breakdown by Income Level",
    'xanchor': "center",
    'yanchor': "top",
    'x': 0.44,
    'y': 0.99,
    'font_size': 20
})
fig.show()

t2 = time.time()
print(t2-t1) # total processing time

# changed the theme colour for good vision purposes.

0.20844483375549316

Interpretation

• The following spyder chart or radial chart shows the Target area breakdown by Income Level for all the countries in the world.
• The radius circular counts represents the percentage that each Income Level contributes to the target area.

Insights / Trends

• From the chart we can observe that
  • Only Low Income Countries are prefering Liquidity because it is obvious that low income countries in general do not have any cash available instantaneously.
  • Banking Sector measures were taken the most among all categories of countries.
  • Measures related to Insolvency were not taken by any category of the countries which is interesting.
• These interpretations can be determined easily with the help of curves intersecting each and every categorical lines.

Choice of Visualization

This would be the most convinient method to visualize the graph because we can easily identify the percentage of the measures for countries with different income categories looking at the graph and the visual comparision would be very easy which inturn makes it very easy for the non - technical people to understand the graph better with out any efforts

# Quantitative Analysis......

# Created a new table from the existing data set using 'R' language and wriiten that to a new csv file in order to
# get Quantitative data since the previous data set has only Qualitative columns.

# reading the newly created file with absolute path
# This file is created such that the count of measures taken by each country in a particular month in the year 2020.

covid_pol_cat = pd.read_csv(r"C:\Users\HP\Downloads\GroupProject_VisualizingCOVIDEconomicData2\GroupProject_VisualizingCOVIDEconomicData\data\covid-pol-count-country-month.csv")
covid_pol_cat

	Country Name	Month	Banking sector	Liquidity/funding	Payment systems	Financial Markets/NBFI	Insolvency
0	Afghanistan	4	5	1	0	0	0
1	Afghanistan	5	0	0	2	0	0
2	Albania	3	3	2	0	0	0
3	Albania	4	1	0	2	1	0
4	Albania	5	5	0	0	0	1
...	...	...	...	...	...	...	...
730	Zimbabwe	3	2	4	0	1	0
731	Zimbabwe	4	1	2	0	0	0
732	Zimbabwe	5	7	2	0	0	0
733	Zimbabwe	6	0	2	1	1	0
734	Zimbabwe	7	0	1	0	0	0

735 rows × 7 columns

# getting count for the famous countries

covid_uk_pol_cat = covid_pol_cat[covid_pol_cat["Country Name"]=="United Kingdom"]
covid_us_pol_cat = covid_pol_cat[covid_pol_cat["Country Name"]=="United States"]
covid_bra_pol_cat = covid_pol_cat[covid_pol_cat["Country Name"]=="Brazil"]
covid_ind_pol_cat = covid_pol_cat[covid_pol_cat["Country Name"]=="India"]
covid_jap_pol_cat = covid_pol_cat[covid_pol_cat["Country Name"]=="Japan"]

covid_uk_pol_cat

	Country Name	Month	Banking sector	Liquidity/funding	Payment systems	Financial Markets/NBFI	Insolvency
690	United Kingdom	3	10	6	0	1	3
691	United Kingdom	4	10	4	1	0	1
692	United Kingdom	5	9	1	1	1	0
693	United Kingdom	6	1	1	2	1	1
694	United Kingdom	7	5	0	0	0	0
695	United Kingdom	8	1	1	1	0	0
696	United Kingdom	9	4	0	0	1	0
697	United Kingdom	10	2	0	0	1	0
698	United Kingdom	11	2	0	0	0	0
699	United Kingdom	12	2	0	0	0	0

# Profile of Economic policy types in the USA from March-December 2020

plt.stackplot(covid_us_pol_cat["Month"],
             covid_us_pol_cat["Banking sector"],
             covid_us_pol_cat["Liquidity/funding"],
             covid_us_pol_cat["Payment systems"],
             covid_us_pol_cat["Financial Markets/NBFI"],
             covid_us_pol_cat["Insolvency"],
             labels=list(covid_us_pol_cat.columns)[2:])
plt.legend()
plt.xticks(covid_us_pol_cat["Month"],["Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"])
plt.title("Profile of Economic policy types in the USA from March-December 2020")

Text(0.5, 1.0, 'Profile of Economic policy types in the USA from March-December 2020')

# Profile of Economic policy types in India from February-December 2020

plt.stackplot(covid_ind_pol_cat["Month"],
             covid_ind_pol_cat["Banking sector"],
             covid_ind_pol_cat["Liquidity/funding"],
             covid_ind_pol_cat["Payment systems"],
             covid_ind_pol_cat["Financial Markets/NBFI"],
             covid_ind_pol_cat["Insolvency"],
             labels=list(covid_ind_pol_cat.columns)[2:])
plt.legend()
plt.xticks(covid_ind_pol_cat["Month"],["Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"])
plt.title("Profile of Economic policy types in India from February-December 2020")

Text(0.5, 1.0, 'Profile of Economic policy types in India from February-December 2020')

Interpretation

• The following two area graphs illustrates the aggregate of policy measures taken in the initial months in US and India respectively.
• Each color represents one category out of all five targeted areas.

Insights / Trends

From the graph we can observe that

• India had more policy measures related to financial markets and nonbank financial institutions than the US during the year 2020.
• By far the largest target area was the banking sector for both the countries but the second largest in USA is liquidity/funding, however in India it was financial markets or non-banking financial institutes.
• In India first drop off in policy count accrued in May whereas in the USA the first drop-off occurred in April.
• Drop-off in policy counts occur was sharper in India in May whereas in you the policy count was tapered down throughout the summer (i.e. Apr, May, Jun, July)
• These interpretations can be determined easily with the help of different colors assigned to each targeted area.
• This gives insights about how each government reacted differently to the global COVID-19 pandemic.

Choice of Visualization

This chart would be the most convenient method to analyze how the policy counts under different targeted areas changes over time using different colors. This way we can also compare the policy count change among the countries just to see how different government responded to COVID-19 in terms of economic measures.

# Monthy Global Economic Policy by Category in the year 2020

covid_international_pol = covid_pol_cat.groupby(["Month"]).sum()
plt.stackplot(list(np.arange(12)),
             covid_international_pol["Banking sector"],
             covid_international_pol["Liquidity/funding"],
             covid_international_pol["Payment systems"],
             covid_international_pol["Financial Markets/NBFI"],
             covid_international_pol["Insolvency"],
             labels=list(covid_international_pol.columns))
plt.legend()
plt.xticks(list(np.arange(12)),["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"])
plt.title("Monthy Global Economic Policy by Category in the year 2020")

Text(0.5, 1.0, 'Monthy Global Economic Policy by Category in the year 2020')

Interpretation

• The preceding stacked area charts represent the number of policy changes enacted by month for the given countries, and the world as a whole.
• The y axis measures the number of policy changes which occurred each month, color-coded by their category. The months are listed along the x-axis.

Insights / Trends

• From the chart we can observe that
  • There was a distinct spike in economic policy changes which occured in early March, essentially reflecting a flurry of economic policy activity during the initial wave of the COVID pandemic.
  • Most countries saw a drop-off in overall economic activity following the initial outbreak of COVID, while some countries, such as India saw a resurgence in economic policy changes during the months of August and September. This is likely due to those countries choosing to loosen COVID restrictions during these months.
  • Some countries enacted new policies relating to the transition to online and remote payment systems. Noteably, the US did not enact any specific policies regarding incentivizing online payment systems, likely because such systems were already widespread before the onset of COVID.

Choice of Visualization

This would be the most convinient method to visualize the graph because we can easily identify the number of the policy measures taken for all the countries in a particular year, and looking at the graph, the visual comparision would be very easy which inturn makes it very easy for the non - technical people to understand the graph better with out any efforts

Potential Sources of Error / Bias

With regards to the financial-policies data visualizations, there are two main possible sources of error. The first is an error in economic reporting from the countries who enacted these policies. Many countries might have made economic policies in secret, or in such a way that they were not widely reported and were not added to this data-set by the people compiling it. The second source of error is human error on the part of the group who compiled this data. It is possible that they miscategorized economic policies, or simply ran up against economic policies which did not cleanly fit into (just) a single category.

Finally, there is the fact that the economic policies listed included extensions of existing policy, implementations of new policy, and retractions of old policies. This means that the running policy count in these charts is for the total activity (in terms of number of policies altered) associated with economic policies of a particular category, not a running count of the total number of active economic policies.

Conclusion

The COVID-19 pandemic was an unprecedented exogenous shock that required immediate policy implementation targeted to the financial sector in order to stabilize the economy. Our analysis found that globally, the highest frequency of policy actions were during the first few months of COVID-19 in 2020, and tapered off a bit during the summer months. Specifically, the most common policy target areas overall were found to be the banking sector and liquidity/funding. The three remaining target areas, which comprise of payment systems, financial markets and nonbank financial institutions, and insolvency, saw significantly less policy action.

When broken down to the country level, we did not necessarily find the same trends as the overall global trend. Specifically, looking at the data for India’s policy measures, their largest target areas were the banking sector and financial markets/nonbank financial institutions. Interestingly, India also had the highest policy measure count of any country. On the other hand, the US more closely mimicked the global data as far as counts go, with the highest number of policy measures targeted to the banking sector and liquidity/funding. We also found that the key target areas differed by income level. Higher income countries tended to target the banking sector more frequently, whereas lower income countries frequently targeted liquidity/funding and payment systems.

These findings not only demonstrate the urgency of implementing financial policy measures to mitigate the COVID-19 pandemic, but that the needs of each country differ significantly depending on their existing economies and situations. While we are able to see a global trend, the trends of each individual economy vary as they each have different needs. While one policy measure may work for a particular country, it might not for another. This underscores the necessity of utilizing informed, data-driven analyses to guide financial policy implementation during an economic crisis.